Comment by PaulHoule

To praise TikTok it has a highly effective recommendation engine precisely because it is showing you one piece of content at a time and registering your engagement on that.

YouTube's interface gives people a feeling of agency because it tempts you with 10 or so videos on the side and you can choose one, it also means YouTube does not get information about the 9 you didn't click, maybe you would have liked 5 of them and hated 4 of them but it can at best guess about that. I read about negative sampling in the recommender literature to address this issue and never felt I understood it or believed in it -- the literature clearly indicates that it sorta-kinda works but I think it does not work very well.

So far as hating on algorithmic feeds it is not the algorithms themselves that are bad but how they are chosen. If there is any characteristic of the content that can be quantified or evaluated a feed can be designed to privilege that. A feed could be designed to be highly prosocial, calming and such or designed to irritate you as much as possible. It's possible that people get bored with the first.

My own reader works like TikTok in that it shows one content piece of the time but it is basically the stuff that I submit to HN and it is scientific papers and articles about LLMs and programming languages and social psychology and political science and sports and and advanced manufacturing and biotech and such. You might say my world view is unusual or something but it is certainly not mindless lowest common denominator stuff or outrage (e.g. to be fair I post a few things to HN because YOShInOn thinks they are spicy -- YOShInOn has a model that can predict if y'all are going to comment on an article or not and I felt it was a problem that my comments/submission ratio was low before I had YOShInOn)